MarginalKron.predict(Xnew, point=None, diag=False, pred_noise=False, model=None)[source]#

Return the mean vector and covariance matrix of the conditional distribution as numpy arrays, given a point, such as the MAP estimate or a sample from a trace.


Function input values. If one-dimensional, must be a column vector with shape (n, 1).

pointpymc.Point, optional

A specific point to condition on.

diagbool, default False

If True, return the diagonal instead of the full covariance matrix.

pred_noisebool, default False

Whether or not observation noise is included in the conditional.

modelModel, optional

Model with the Gaussian Process component for which predictions will be generated. It is optional when inside a with context, otherwise it is required.